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Evaluate Skill

OrganizationPopular
every-app
evaluate-skill

Test a candidate OpenSEO skill end to end by running fresh, isolated Codex sessions against the local backend and scoring the reports they save. Use when editing a product skill (seo-audit, keyword-research, ...) and you need evidence that the new instructions produce better output, not just a cleaner file.

Overview

Publisherevery-app
Repositoryopen-seo
Skill nameevaluate-skill
Stars
22K
Forks
2.8K
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by every-app on GitHub. Read the source before you install it.

Installation

Install the Evaluate Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/every-app/open-seo/tree/main/.agents/skills/evaluate-skill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/every-app/open-seo.git /tmp/open-seo
mkdir -p .claude/skills
cp -r /tmp/open-seo/.agents/skills/evaluate-skill .claude/skills/evaluate-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evaluate Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evaluate Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evaluate Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Evaluate a skill

A skill edit is only as good as the reports it produces. This workflow runs the candidate skill in sessions that know nothing except the skill file, a one-line business brief, and a locked-down local MCP endpoint, then compares what came back.

Prerequisites

  • Local server in no-auth mode: AUTH_MODE=local_noauth in .env.local, then pnpm db:migrate:local and pnpm dev (or pnpm dev:agents). Pass the URL it prints, with /mcp appended, as --endpoint. Default local D1, never remote bindings or remote Postgres. Keep it private; it has no login.
  • codex CLI on the path. Runs use its configured default model at high reasoning; record the resolved model from the events log if you need to name it.
  • Research credits: the local server still calls DataForSEO. An audit run makes 20 to 40 tool calls. Do not run more sessions than the comparison needs.

Run

sh
node .agents/skills/evaluate-skill/scripts/run.mjs \
  --skill seo-audit \
  --site example.com --site holdout.example \
  --brief "Evaluation brief: Acme at example.com sells <what>. The goal is relevant organic visitors who could become customers. No first-party analytics are connected." \
  --brief "Evaluation brief: <the holdout business>. ..." \
  --endpoint http://<local-dev-host>/mcp

Runs take about 10 to 14 minutes and execute in parallel. Launch the script detached, because a tool-call timeout that kills the shell kills the sessions too:

sh
(nohup node .agents/skills/evaluate-skill/scripts/run.mjs ... > .logs/eval.log 2>&1 &)

What the script does, per site:

  1. Copies the candidate skill and seo-report into a fresh temp folder. Edits during the run cannot change its instructions; the manifest records the skill hashes.
  2. Creates a new local project seeded only with the brief.
  3. Starts a gateway in front of the local MCP that allows a fixed tool list and rejects any call for another project id, then probes that rejection before starting.
  4. Runs codex exec ephemeral, ignoring user config, memories, apps, plugins, and multi-agent, with the skill's self-review as the only reviewer.
  5. Captures the saved report HTML, every MCP call and response, the event stream, and the final message under the output folder.

Read the reports with node .agents/skills/evaluate-skill/scripts/report-text.mjs <out>/*-report.html. Each session's working notes (for seo-audit, opportunities.md and evidence/) stay in its temp folder, named in manifest.json.

Isolation rules

  • Instructions: frozen copies only. Never point a session at the working tree.
  • Conversation: a new codex exec per run. Never resume or fork.
  • Data: a new project per run, seeded with the user-provided brief and nothing else. Never write evaluator notes, prior reports, or a reference answer into any project the sessions can read.
  • Prompt: the same neutral prompt for every variant. Do not name expected findings, target keywords, or the rubric.
  • Evaluator material (rubric, reference audits, prior reports) lives outside the session folders and outside the repository. Raw artifacts belong in the ignored .logs/.

This is practical isolation, not a security boundary: sessions share the local account, provider caches, and organization limits.

Compare

Always run at least two sessions of the site you are tuning on and one holdout site with a different shape, so an instruction that fits one company's answer is caught. Keep every run, including failures; do not pick the best of several and call the skill fixed.

Score each report before opening its tool trace, then read the trace to classify any miss as discovery, tool, reasoning, or reporting. For an audit report:

DimensionWeightA strong report...
Discovery and coverage25%Reads commercial pages and their siblings, tools, and technical paths; broadens when a signal appears
Diagnosis and evidence20%Checks live pages and live results; separates observations from causes; records source, date, and settings
Business value and scale20%Ties each action to a real buyer; compares competing opportunities; never invents conversion or revenue
Prioritization15%Leads with a bounded change to a demonstrated problem for likely buyers; keeps maintenance and broad programs back
Calibration10%Makes useful calls without analytics; no penalty, causation, or trend claims the evidence cannot carry
Communication and actionability10%Names the page, the change, and the reason; material findings survive compression

A material factual error overrides the score. Ask a second model to recompute every live position from the run's own MCP log before trusting a report's numbers.

Coverage checks that generalize beyond any one site:

  • Sibling pages in an important family (comparison, pricing, template, location) were read side by side, and a page that only swaps a name into a shared answer was noticed.
  • A page already at the top for its query was protected, not rewritten.
  • Every serious candidate ended as a recommendation or as a table row with a real reason. A vague "later" is a miss.
  • Search volume, visits, and revenue were kept distinct; US demand was not multiplied into a global figure.

What past evaluations taught

  • Instruction length hurts. A 3,600-word skill of cautions performed worse than a 2,200-word skill with a fixed process. Add a step, not a warning.
  • Force the comparison. Sessions that wrote a shortlist before drafting kept their diagnoses; sessions that drafted straight from research ranked by raw query volume.
  • Show the rejected candidates. A "What else we checked" table in the report is what stops material findings from disappearing when the report is shortened.
  • One snapshot lies. Positions around 8 to 12 change between two checks minutes apart; the skill re-runs decisive queries and reports both.
  • Labels beat notes. Readers rejected "10th of 17 organic rows" and arrows; tables now say "#10 (page 1)" with market and date in the header.

Clean up

Each run's project, report, and audit can be removed through MCP with delete_report, delete_site_audit, and update_project_context, or left in the local database. A new project per run is simpler than proving an old one is clean. Stop the local server when finished.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evaluate Skill AI skill do?

Test a candidate OpenSEO skill end to end by running fresh, isolated Codex sessions against the local backend and scoring the reports they save. Use when editing a product skill (seo-audit, keyword-research, ...) and you need evidence that the new instructions produce better output, not just a cleaner file.

Why use Evaluate Skill on TypingMind?

Because you install it once and use it with any model. Evaluate Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evaluate Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/every-app/open-seo/tree/main/.agents/skills/evaluate-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evaluate Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evaluate Skill?

As many as you like. As long as a model supports skills, you can use Evaluate Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evaluate Skill AI skill free?

Yes. It is published on GitHub by every-app under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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